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Abstract Topology optimization has emerged as a versatile design tool embraced across diverse domains. This popularity has led to great efforts in the development of education-centric topology optimization codes with various focuses, such as targeting beginners seeking user-friendliness and catering to experienced users emphasizing computational efficiency. In this study, we introduce , a novel 2D and 3D topology optimization software developed in Python and built upon the open-source library, designed to harmonize usability with computational efficiency and post-processing for fabrication. employs a modular architecture, offering a unified input script for defining topology optimization problems and six replaceable modules to streamline subsequent optimization tasks. By enabling users to express problems in the weak form, eliminates the need for matrix manipulations, thereby simplifying the modeling process. The software also integrates automatic differentiation to mitigate the intricacies associated with chain rules in finite element analysis and sensitivity analysis. Furthermore, provides access to a comprehensive array of readily available solvers and preconditioners, bolstering flexibility in problem-solving. is designed for scalability, furnishing robust support for parallel computing that seamlessly adapts to diverse computing platforms, spanning from laptops to distributed computing clusters. It also facilitates effortless transitions for various spatial dimensions, mesh geometries, element types, and orders, and quadrature degrees. Apart from the computational benefits, facilitates the automated exportation of optimized designs, compatible with open-source software for post-processing. This functionality allows for visualizing optimized designs across diverse mesh geometries and element shapes, automatically smoothing 3D designs, and converting smoothed designs into STereoLithography (STL) files for 3D printing. To illustrate the capabilities of , we present five representative examples showcasing topology optimization across 2D and 3D geometries, structured and unstructured meshes, solver switching, and complex boundary conditions. We also assess the parallel computational efficiency of by examining its performance across diverse computing platforms, process counts, problem sizes, and solver configurations. Finally, we demonstrate a physical 3D-printed model utilizing the STL file derived from the design optimized by . These examples showcase not only ’s rich functionality but also its parallel computing performance. The open-source is given in Appendix B and will be available on GitHub.
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An optimized transient detection pipeline for the ASKAP Variables and Slow Transients (VAST) survey
ABSTRACT In this paper, we present an optimized version of the detection pipeline for the ASKAP Variables and Slow Transients (VAST) survey, offering significant performance improvement. The key to this optimization is the replacement of the original w-projection algorithm integrated in the Common Astronomy Software Applications package with the w-stacking algorithm implemented in the WSClean software. Our experiments demonstrate that this optimization improves the overall processing efficiency of the pipeline by approximately a factor of 3. Moreover, the residual images generated by the optimized pipeline exhibit lower noise levels and fewer artefact sources, suggesting that our optimized pipeline not only enhances detection accuracy but also improves imaging fidelity. This optimized VAST detection pipeline is integrated into the Data Activated Liu Graph Engine (DALiuGE) execution framework, specifically designed for SKA-scale big data processing. Experimental results show that the performance and scalability advantages of the pipeline using DALiuGE over traditional MPI or BASH techniques increase with the data size. In summary, the optimized transient detection pipeline significantly reduces runtime, increases operational efficiency, and decreases implementation costs, offering a practical optimization solution for other ASKAP imaging pipelines as well.
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- Award ID(s):
- 1816492
- PAR ID:
- 10545358
- Publisher / Repository:
- Monthly Notices of the Royal Astronomical Society
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 526
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 1809 to 1821
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/mnras/stad2809
